plotMetrics: plotMetrics

In greenelab/miQC: Flexible, probabilistic metrics for quality control of scRNA-seq data

plotMetrics

Description

A function to plot the QC parameters used for a miQC model, number of unique genes expressed and percent mitochondrial reads. This function can be run before calling mixtureModel() to assess if miQC is appropriate given the data distribution. See vignette for examples of cases where miQC is and isn't a good choice for filtering.

Usage

plotMetrics(
sce,
detected = "detected",
subsets_mito_percent = "subsets_mito_percent",
palette = "#33ADFF"
)

Arguments

sce	(SingleCellExperiment) Input data object.
detected	(character) Column name in sce giving the number of unique genes detected per cell. This name is inherited by default from scater's addPerCellQC() function.
subsets_mito_percent	(character) Column name in sce giving the percent of reads mapping to mitochondrial genes. This name is inherited from scater's addPerCellQC() function, provided the subset "mito" with names of all mitochondrial genes is passed in. See examples for details.
palette	(character) Specifies the color to plot cells as. Default is "#33ADFF".

Value

Returns a ggplot object. Additional plot elements can be added as ggplot elements (e.g. title, customized formatting, etc).

Examples

library(scRNAseq)
library(scater)
sce <- ZeiselBrainData()
mt_genes <- grepl("^mt-",  rownames(sce))
feature_ctrls <- list(mito = rownames(sce)[mt_genes])
sce <- addPerCellQC(sce, subsets = feature_ctrls)
plotMetrics(sce)

greenelab/miQC documentation built on Jan. 6, 2023, 12:16 a.m.